System and method for tissue viability screening

ABSTRACT

A system for determining the viability of an embryo comprises an imaging device, an excitation device configured to direct an excitation energy at an embryo, a controller communicatively connected to the imaging device and the excitation device, configured to drive the excitation device and collect images from the imaging device at an imaging frequency, a processor performing steps comprising acquiring a set of images from the imaging device, performing a Fourier Transformation to generate a set of phasor coordinates, computing a D-trajectory, computing a set of values of additional parameters, comparing the set of values to a set of stored values related to embryos of known viability, and calculating a viability index factor of the embryo from the set of values and the set of stored values. Methods of calculating embryo viability and determining one or more properties of a tissue are also described.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.16/839,463, filed on Apr. 3, 2020, which claims priority to U.S.Provisional Patent Application No. 62/828,848, filed on Apr. 3, 2019,each of which are incorporated herein by reference in their entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under grant nos.R21HD090629 and 2P41GM103540 from the National Institutes of Health. Thegovernment has certain rights in the invention.

REFERENCE TO A “SEQUENCE LISTING”, A TABLE, OR A COMPUTER PROGRAMLISTING APPENDIX SUBMITTED AS AN XML FILE

The present application hereby incorporates by reference the entirecontents of the XML file named “206101-0026-01US_SequenceListing.xml” inXML format, which was created on Nov. 10, 2022, and is 4,648 bytes insize.

BACKGROUND OF THE INVENTION

It is estimated that more than 8 million babies have been born worldwidesince the first in-vitro fertilization (IVF) baby was born in 1978. Thelatest figure is that around 1.5 million Assisted ReproductiveTechnology (ART) cycles are performed each year worldwide, with anestimated 350,000 babies born.

Determining embryo quality during in vitro fertilization (IVF) is one ofthe most important steps toward successful pregnancy. The standardnon-invasive method to assess embryo quality and viability relies on thevisual inspection of embryo morphology according to predefined criteriasuch as cell division patterns, the number of pronucleoli in cleavagestages, and the physical characteristics of the blastocyst. Assistedreproduction through morphological evaluation is labor-intensive andhighly dependent on the performance of individual physicians trained inthese techniques. Development of more quantitative and objective meansfor assessing embryo quality that are simpler, safer, and faster couldprovide significant advantages in assisted reproduction by enablingsingle embryo transfers rather than the implantation of multiple embryosin order to increase the likelihood of a successful pregnancy.

Given the limitations of morphological evaluation, several technologieshave been explored for the assessment of embryo viability. These includethe measurement of metabolites in embryonic culture media, as well asgenomic and proteomic profiling of the embryos themselves. Otherspectroscopic technologies have emerged as a non-invasive means ofrevealing embryo viability via the detection of various metabolic statesof common molecules associated with embryo development. Raman,near-infrared, Nuclear Magnetic Resonance (NMR), and Fourier-transforminfrared spectroscopy can also detect the metabolic states of pyruvate,lactate, glucose, and oxygen during pre-implantation mammaliandevelopment (see e.g. E. Seli et al., Noninvasive metabolomic profilingof embryo culture media using Raman and near-infrared spectroscopycorrelates with reproductive potential of embryos in women undergoing invitro fertilization. Fertil Steril 88, 1350-1357 (2007); C. G. Vergouwet al., Metabolomic profiling by near-infrared spectroscopy as a tool toassess embryo viability: a novel, non-invasive method for embryoselection. Hum Reprod 23, 1499-1504 (2008); and E. Seli et al.,Noninvasive metabolomic profiling as an adjunct to morphology fornoninvasive embryo assessment in women undergoing single embryotransfer. Fertil Steril 94, 535-542 (2010), all incorporated herein byreference). However, at the present time these technologies suffer froma number of shortcomings. It is challenging for these approaches toanalyze the data in the short time window needed for the host transferof embryos. The data analyses are technically demanding and may not beintuitively obvious for the general clinical use. The technologiesrequire fluid samples collected from the embryo culture media and thedata are inherently noisier.

Fluorescence Lifetime Imaging Microscopy (FLIM) produces an image, basedon the exponential decay rates at each pixel from a fluorescent sample.The fluorescence lifetime of the fluorophore signal is measured tocreate the image via FLIM. An exemplary illustration of phasor FLIManalysis is shown in FIG. 2 . During FLIM collection, a pulsed 2-photonlaser is used to measure the intensity at short time windows (timearrival of the photons) as a function of time. Instead of fitting thedecay curve into an exponential equation (black line in graph 201), theraw data (intensity at each pixel) is transformed into polar coordinatesby plotting the sine (red line) and cosine (blue line) using Fouriertransformation, for every pixel in the object, the fluorescence lifetimecan be obtained as “phasor lifetime” Phasor graph 202 depicts anexemplary phasor fingerprint of pure intrinsic biomarkers of freenicotinamide adenine dinucleotide (NADH) in solution, bound NADH in thepresence of lactate dehydrogenase, and a long lifetime species derivedfrom lipid droplets. Given that the free form of NADH exhibits a compactstructure with a low fluorescence quantum yield (q)=0.019) and a shortlifetime of 0.4 ns and the extended form of NADH bound to lactatedehydrogenase with a much higher quantum yield (φ=0.099) with a longerfluorescence lifetime up to −3.4 ns, the lifetimes of these two statescan be easily distinguished. Based on the law of phasor addition, anysample containing the combination signature of these three species willfall within the triangle joining the three phasors.

When FLIM is coupled with two-photon excitation microscopy, moleculesare excited at longer wavelengths (with lower energy photons). Thisprevents photodamage and allows deeper imaging, resulting in superiorimage quality. Since endogenous molecules such as collagen, retinoids,flavins, folate and NADH are fluorescent in live cells, fluorescencelifetime data can be used to identify these intrinsic fluorescentspecies. The contributions from these different biochemical species areindicators of an embryo's biochemical property. In the disclosedapproach, the fluorescent lifetime signal from integrated images isacquired, and the raw data is transformed using a Fourier transformationto yield the average arrival time of emitted photons in each pixel,represented by polar coordinates “g” and “s” in the transformationfunction (Graph C in FIG. 3 , FIG. 2 ). This allows the data to bepresented in a two-dimensional graphical representation of the lifetimedistributions, known as the phasor plot, for each pixel in the FLIMimage (see FIG. 2 ).

Development of qualitative and objective means for assessing embryoquality and viability that are safer and faster will provide significantadvances in IVF and animal breeding facilities. There is a need for afaster, safer, and objective method to assess embryo quality, in orderto improve outcomes in assisted reproductive technology (ART) for IVFand animal breeding. The present invention satisfies this need.

SUMMARY OF THE INVENTION

In one aspect, a system for determining the viability of an embryocomprises an imaging device, an excitation device configured to directan excitation energy at an embryo, a controller communicativelyconnected to the imaging device and the excitation device, configured todrive the excitation device at an excitation frequency and collectimages from the imaging device at an imaging frequency, a non-transitorycomputer-readable medium with instructions stored thereon, that whenexecuted by a processor perform steps comprising acquiring a set ofimages from the imaging device, performing a Fourier Transformation onthe set of images to generate a set of phasor coordinates, computing aD-trajectory of the phasor coordinates, computing a set of values ofadditional parameters from the set of images and the phasor coordinates,comparing the set of values to a set of stored values related to embryosof known viability, and calculating a viability index factor of theembryo from the set of values and the set of stored values.

In one embodiment, the excitation energy is laser light. In oneembodiment, the system further comprises a heat blocker positionedbetween the excitation source and the embryo, configured to preventexcessive heating of the embryo by the excitation source. In oneembodiment, the imaging device is a fluorescence lifetime imagingmicroscope. In one embodiment, the additional parameters are selectedfrom the group consisting of coordinates for a center of mass g and s,second axial moments a and b after diagonalization, an angle ofdistribution from diagonalization, and a total number of pixels in thephasor plot from slices derived from the phasor coordinates. In oneembodiment, the excitation frequency is about 20 MHz. In one embodiment,the imaging device has a plurality of taps, and the instructionscomprise the step of acquiring multiple images from the imaging devicesimultaneously using the plurality of taps. In one embodiment, theimaging device comprises two taps.

In another aspect, a method of calculating an embryo viability indexcomprises the steps of exciting an embryo with an excitation energy froman excitation source at an excitation frequency, acquiring a set ofimages of the embryo from an imaging device, performing a FourierTransformation on the set of images to generate a set of phasorcoordinates, computing a D-trajectory of the phasor coordinates,computing a set of values of additional parameters from the set ofimages and the phasor coordinates, comparing the set of values to a setof stored values related to embryos of known viability, and calculatinga viability index factor of the embryo from the set of values and theset of stored values. In one embodiment, a power density of theexcitation energy is greater than about 1 mW/nm and less than about 3.5mW/nm. In one embodiment, the excitation energy is laser light having aspectral range between 390 nm and 2000 nm. In one embodiment, theparameters are selected from the group consisting of coordinates for acenter of mass g and s, second axial moments a and b afterdiagonalization, an angle of distribution from diagonalization, and atotal number of pixels in the phasor plot from slices derived from thephasor coordinates.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing purposes and features, as well as other purposes andfeatures, will become apparent with reference to the description andaccompanying figures below, which are included to provide anunderstanding of the invention and constitute a part of thespecification, in which like numerals represent like elements, and inwhich:

FIG. 1 is a schematic diagram of a system of the invention;

FIG. 2 is an illustrative example of a phasor transformation;

FIG. 3 is a diagram of a method of the invention;

FIG. 4 is a method of the invention;

FIG. 5A is a set of images and graphs related to experimental data;

FIG. 5B is a set of images and graphs related to experimental data;

FIG. 5C is a set of images and graphs related to experimental data;

FIG. 6A is a set of transmission and FLIM images related to experimentaldata;

FIG. 6B is a set of FLIM images and graphs related to experimental data;

FIG. 7A is a set of phasor and scatter plots related to experimentaldata;

FIG. 7B is a set of images and graphs related to experimental data;

FIG. 8 is a set of images related to experimental data;

FIG. 9 is a set of images and graphs related to experimental data;

FIG. 10A is a set of images and graphs related to experimental data;

FIG. 10B is a set of images and graphs related to experimental data;

FIG. 10C is a set of graphs related to experimental data;

FIG. 10D is a set of images and graphs related to experimental data;

FIG. 10E is a set of graphs related to experimental data;

FIG. 11A is a set of images and graphs related to experimental data;

FIG. 11B is a set of images and graphs related to experimental data;

FIG. 11C is a set of graphs related to experimental data; and

FIG. 11D is a set of graphs related to experimental data.

DETAILED DESCRIPTION

It is to be understood that the figures and descriptions of the presentinvention have been simplified to illustrate elements that are relevantfor a clear understanding of the present invention, while eliminating,for the purpose of clarity, many other elements found in related systemsand methods. Those of ordinary skill in the art may recognize that otherelements and/or steps are desirable and/or required in implementing thepresent invention. However, because such elements and steps are wellknown in the art, and because they do not facilitate a betterunderstanding of the present invention, a discussion of such elementsand steps is not provided herein. The disclosure herein is directed toall such variations and modifications to such elements and methods knownto those skilled in the art.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Although any methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of the present invention, exemplary methods andmaterials are described.

As used herein, each of the following terms has the meaning associatedwith it in this section.

The articles “a” and “an” are used herein to refer to one or to morethan one (i.e., to at least one) of the grammatical object of thearticle. By way of example, “an element” means one element or more thanone element.

“About” as used herein when referring to a measurable value such as anamount, a temporal duration, and the like, is meant to encompassvariations of ±20%, ±10%, ±5%, ±1%, and ±0.1% from the specified value,as such variations are appropriate.

Throughout this disclosure, various aspects of the invention can bepresented in a range format. It should be understood that thedescription in range format is merely for convenience and brevity andshould not be construed as an inflexible limitation on the scope of theinvention. Accordingly, the description of a range should be consideredto have specifically disclosed all the possible subranges as well asindividual numerical values within that range. For example, descriptionof a range such as from 1 to 6 should be considered to have specificallydisclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numberswithin that range, for example, 1, 2, 2.7, 3, 4, 5, 5.3, 6 and any wholeand partial increments therebetween. This applies regardless of thebreadth of the range.

In some aspects of the present invention, software executing theinstructions provided herein may be stored on a non-transitorycomputer-readable medium, wherein the software performs some or all ofthe steps of the present invention when executed on a processor.

Aspects of the invention relate to algorithms executed in computersoftware. Though certain embodiments may be described as written inparticular programming languages, or executed on particular operatingsystems or computing platforms, it is understood that the system andmethod of the present invention is not limited to any particularcomputing language, platform, or combination thereof. Software executingthe algorithms described herein may be written in any programminglanguage known in the art, compiled or interpreted, including but notlimited to C, C++, C#, Objective-C, Java, JavaScript, Python, PHP, Perl,Ruby, or Visual Basic. It is further understood that elements of thepresent invention may be executed on any acceptable computing platform,including but not limited to a server, a cloud instance, a workstation,a thin client, a mobile device, an embedded microcontroller, atelevision, or any other suitable computing device known in the art.

Parts of this invention are described as software running on a computingdevice. Though software described herein may be disclosed as operatingon one particular computing device (e.g. a dedicated server or aworkstation), it is understood in the art that software is intrinsicallyportable and that most software running on a dedicated server may alsobe run, for the purposes of the present invention, on any of a widerange of devices including desktop or mobile devices, laptops, tablets,smartphones, watches, wearable electronics or other wirelessdigital/cellular phones, televisions, cloud instances, embeddedmicrocontrollers, thin client devices, or any other suitable computingdevice known in the art.

Similarly, parts of this invention are described as communicating over avariety of wireless or wired computer networks. For the purposes of thisinvention, the words “network”, “networked”, and “networking” areunderstood to encompass wired Ethernet, fiber optic connections,wireless connections including any of the various 802.11 standards,cellular WAN infrastructures such as 3G or 4G/LTE networks, Bluetooth®,Bluetooth® Low Energy (BLE) or Zigbee® communication links, or any othermethod by which one electronic device is capable of communicating withanother. In some embodiments, elements of the networked portion of theinvention may be implemented over a Virtual Private Network (VPN).

Although certain embodiments of systems and methods disclosed herein maybe described in reference to determining embryo viability, it isunderstood that the systems and methods may be used for other processes,for example to determine properties of one or more cells, tissues, orliving organisms more generally. Suitable examples include, but are notlimited to, identifying changes in metabolism due to cell cycle, stress,cancer diabetes, and neurodegenerative diseases within cell, tissue,and/or blood samples.

Certain elements of the disclosed invention constitute improvements overprior published work, including Ma et al., Label-free assessment ofpre-implantation embryo quality by the Fluorescence Lifetime ImagingMicroscopy (FLIM)-phasor approach,https://www.biorxiv.org/content/early/2018/03/22/286682.full.pdf, Mar.22, 2018; updated version of the samehttps://www.biorxiv.org/content/10.1101/286682v2, published online Apr.21, 2018; final version published in Scientific Reports vol. 9, 13206(Sep. 13, 2019); and Chen et al., Widefield multi-frequency fluorescencelifetime imaging using a two-tap complementary metal-oxide-semiconductor(CMOS) camera with lateral electric field charge modulators, Journal ofbiophotonics, Nov. 12, 2018; all of which are incorporated herein byreference.

A conceptual illustration of an imaging device of the present inventionis shown in FIG. 1 . An imaging device 101 is positioned to gatherimaging data from a sample through microscope optics. The imaging devicemay in some embodiments comprise a CMOS camera, based on the lateralelectric field charge modulator (LEFM) technique. In some embodiments,the imaging device may comprise one or more of a modulated CMOS camera,a charged coupled device (CCD), an electron-multiplying CCD (EMCCD), anintensified CCD, a photomultiplier tube (PMT), an avalanche photodiode(APD), or a single-photon avalanche diode (SPAD). It is understood thatan “imaging device” as described herein could be any imaging device,including but not limited to a camera, a scanning device, a microscope,for example a fluorescence lifetime imaging microscope (FLIM), or apoint detector.

The imaging device may be physically attached to a camera port of amicroscope, or in some embodiments, all or part of the imaging devicemay be incorporated into a microscope. In the depicted example, themicroscope is an Olympus TIRF microscope. An excitation source 103 ispositioned to provide excitation and/or illumination to the sample114—in the depicted example, the excitation source is a white-lightsupercontinuum laser (SC390, Fianium, Inc), but it is understood thatany suitable excitation or illumination source could be used, includingbut not limited to lasers in different color regions, laser diodes,light emitting diodes (LEDs), photon lasers, photon excitation lasers,specific light sources, lamps, or multiphoton excitation sources. Anexcitation source may have a spectral range, for example a broadspectral range of between 390 nm to 2000 nm. In other embodiments, anarrower or more specific spectral range may be used, for examplelimited to certain color bands in the visible, ultraviolet, and/orinfrared spectrum. Suitable exemplary spectral ranges include, but arenot limited to, 380 nm to 740 nm, 450 nm to 980 nm, 500 nm to 740 nm, orany other suitable range or combination of ranges.

The excitation source may in some embodiments include or be used inconjunction with one or more filters or excitation cleaners, for examplea filter wheel 105. Suitable filters include, but are not limited to,low-pass filters, high-pass filters, band-pass filters, heat blockers,filter wheels, or beam splitter glass. In one embodiment, a filter ofeight band-pass filters is used. In some embodiments, the one or morefilters are mounted on a movable device so that the filter or filters inthe excitation path may be adjusted, manually or with a controller. Insome embodiments, the controller includes one or more elements ofcomputer control. In one embodiment, the power density of the excitationor illumination source may be >1 mW/nm. In some embodiments, the powerdensity is between 1 μW/nm and 100 mW/nm, or between 100 μW/nm and 10mW/nm, or between 1 mW/nm and 5 mW/nm. In other embodiments, theexcitation source may be generated at an energy level in a range ofbetween 25 μJ and 25 J, or between 50 μJ and 10 J, or between 100 μJ and5 J, or between 1 mJ and 1 J, or between 10 mJ and 100 mJ, or between2511.1 and 1 mJ, or between 5011.1 and 10 mJ, or between 100 mJ and 10J, or between 1 J and 25 J, or between 10 J and 25 J.

An excitation cleaner 104 may in some embodiments be a heat blocker,which may be provided in the excitation path to reduce certain types onexcitation energy from the excitation source from reaching the sample.In some embodiments, the excitation cleaner is an an infrared (IR)blocker. Although an IR blocker is used in certain exemplary embodimentsdescribed herein, any suitable excitation energy blocking material thatis transparent to one or more wavelengths of light from the excitationsource may be used.

A Total Internal Reflection Fluorescence (TIRF) element 106 may be usedin conjunction with the excitation source and the microscope. In theexemplary embodiment of FIG. 1 , the TIRF element 106 is an Olympus TIRFarm, which is connected to the excitation source 103 via fiber opticcable 107. After entering the microscope 102, the excitation beam may bereflected or diverted by one or more optic elements. In the depictedexample of FIG. 1 , the beam is diverted to a dichoric mirror 108, thenthrough a prism 109, which diverted the beam to illuminate the sample114, which was in turn imaged by imaging device 101. In someembodiments, a synchronization signal 110 may synchronize pulses orperiodic excitation from a modulated excitation source 103 and imageacquisition from an imaging device 101.

In some embodiments, excitation source 103 is a modulated excitationsource having an excitation frequency, which may be synchronized withimaging device 101. Modulation frequencies for a device of the presentinvention include, but are not limited to any frequency suitable for usewith fluorescence-lifetime imaging microscopy (FLIM), for examplebetween 1 MHz and 1 GHz, or between 1 MHz and 200 MHz, or between 1 MHzand 100 MHz, or between 2 MHz and 80 MHz, or between 5 MHz and 50 MHz,or between 10 MHz and 30 MHz, or about MHz, or any other suitablemodulation frequency range.

Synchronization and modulation of the excitation source and the imagingdevice may be controlled either by a controller connected to orintegrated with the excitation source, or by a controller connected toor integrated with the imaging device. In some embodiments, the imagingdevice includes an embedded controller, for example a controllercomprising a microcontroller, ASIC, and/or FPGA. An imaging device 101of the present invention may have multiple “taps”, to provide foraccelerated readout of image data from the sensor by reading frommultiple locations on the pixel sensor simultaneously. In someembodiments, an imaging device of the present invention may have onetap, two taps, four taps, eight taps, or more. In some embodiments,multiple imaging devices may be used, for example with a beam splitteror other image splitting device, in order to gather more image data froma sample at higher speeds.

Where multiple taps are used, the multiple taps may be acquired andcombined based on the phase order in a period, i.e. in one full periodmeasurement, two or more sets of phase images are actually acquired. Thephase steps can be set according to the requirement of harmonics andimaging time. For example, if a high harmonic frequency, for example a31 st harmonic (620 MHz), is needed, the phase steps can be set to 64.As a result, the FLIM imaging time is 64 frames. In some measurements,16 phase steps are used as balance of harmonic contents and FLIM imagingspeed. Images may be acquired via a computer-controlled imageacquisition peripheral, for example a Framelink PCIe card (VCE-CLPCIe01,Imperx, USA). The maximum frame range of this camera is 12 fps. A USBconnection may be used to connect the camera to the computer 112containing the image acquisition peripheral, for example in order tosend control signals or firmware to the imaging device. An embedded FPGAmay be used in some embodiments to perform various operations, e.g.phase control and sweeping. Such a configuration greatly reduces thecomplexity required in daily operation. The acquired phase images 111may then undergo further processing in an image processing software. Inone embodiment, SimFCS software is used. All data may be graphicallyanalyzed by a phasor method, which allows easy fit-free lifetimeanalysis of FLIM images.

A system of the present invention may involve a computing deviceconfigured to process a quantity of data with a machine learningalgorithm. In one embodiment, a portion of data measured from embryoswith known or later discerned viability metrics is used as a trainingset, while a second portion of data measured from embryos with known orlater discerned viability metrics is used as a test set.

In one exemplary embodiment, the FLIM data collected from individualembryos are placed in either of two categories, the H (control/healthygroup has FLIM signature from the embryos developed to the blastocyststage) and UH (sample/unhealthy group has FLIM signature from theembryos arrested at compaction stage or even earlier). The distancealgorithm can generate a “spectra” from the given (up to 24 parameters)of phasor FLIM distributions corresponding to individual embryos. In oneembodiment, the 24 parameters include, but are not limited to, the 2coordinates for the center of mass g and s, 2 second axial moments a andb after diagonalization, the angle of the distribution from thediagonalization and the total number of pixels in the phasor plot fromthe 4 slices of the 3D phasor histogram. For each parameter set, theaverage and standard deviation of the parameters was calculated. then a“distance” function was constructed in which the difference of theaverage of the two sets weighted by the variance of the parameter ineach set for the group H and UH respectively was calculated.

Using distance analysis, a training set can be generated based on thebest weight set that has been chosen to separate the H and UH setembryos according to the distance from the average of each set. In someembodiments, after the training set has been generated the rest of theembryos were tested, and an embryo viability index (EVI) is calculatedfor each embryo. Using the EVI index for the spectra of the trainingset, the data can be binned, for example into a histogram, in order todetermine if a member is a true positive (below 0) or a false positive(above 0). Statistical methods such as the area under the curve (AUC)are then used to determine the quality of the training set. If the AUCis close to 1, the two groups are more separable since there are fewerfalse positives. More details of the distance analysis calculation canbe found in Ranjit et al., Characterizing fibrosis in UUO mice modelusing multiparametric analysis of phasor distribution from FLIM images.Biomedical Optics Express 7, 3519-3530 (2016), incorporated herein byreference.

A method of the present invention applies the phasor-fluorescencelifetime imaging microscopy (FLIM) method and examines the dynamicendogenous biomarker changes during preimplantation embryo development.Based on the quantifiable physiological property changes, the biomarkerchanges are correlated to embryo viability (see FIG. 3 ). Thisnon-invasive phasor-FLIM analysis is sensitive, quick and intuitive.When the disclosed method is applied to pre-implantation mouse embryos,detailed data was captured on their metabolic states at variousdevelopmental stages. At each stage, the mouse embryo displays acharacteristic phasor-FLIM signature.

For the first time, the disclosed method defines a unique graphicalmetabolic trajectory that correlates with energy metabolism and embryodevelopment, referred to herein as the developmental trajectory or“D-trajectory”. Initially, embryos uptake pyruvate during glycolysis astheir main energy source. As the embryos develop to later stages, theneed for ATP increases in order to activate transcription forproliferation. Then, the embryos switch from glycolysis to oxidativephosphorylation, primarily using glucose as their energy source, whichalso changes the relative redox potential (NAD+: NADH ratio). Thespectroscopic signatures from each of these changes are detected and canbe used as criteria to identify healthy embryos at each stage indevelopment. The D-trajectory of pre-implantation embryos cultured innutrient-deficient media deviates significantly from that of thosecultured in normal media, indicating that lifetime trajectories can beused to detect metabolic alterations in embryos. Methods of the presentinvention are able to calculate several different mathematicalparameters that are statistically different between healthy andunhealthy pre-implantation embryos based on machine learninginformation. Therefore, methods of the present invention provide anobjective, non-invasive, and quantitative method to assess the qualityof mammalian embryos.

With reference now to FIG. 3 , an application of a non-invasive liveimaging approach capable of measuring NADH levels within living mouseembryos undergoing in vitro culturing is shown. In step A, embryos arecollected from pre-implantation stages. In step B, intrinsicfluorescence lifetimes are obtained for each embryo using a microscopecoupled with a FLIM box. In step C, the data is analyzed with the phasorapproach, and in step D, a distance analysis is conducted in order topredict embryo viability.

A phasor-FLIM approach is applied, and information is captured onmetabolic energy sources (e.g., NADH) utilized by pre-implantationembryos. A phasor-FLIM signature of free- or bound-NADH changesdynamically as an embryo undergoes specific developmental stages. Whenoxidative phosphorylation is disrupted using complex I and IIIinhibitors, the phasor-FLIM signature is also significantly affected,suggesting that the phasor-FLIM approach is sensitive to the metabolic(free/bound) state of NADH in embryos. In some embodiments, methods ofthe present invention can be used to identify embryos in healthyconditions and in nutrient-lacking “high stress” conditions at a timeperiod before such a morphological difference can be identified by anexpert from a transgenic mouse facility. In one embodiment, thedifference may be identified 24 hrs before it would be detected by suchan expert.

With reference to FIG. 4 , a method of calculating an embryo viabilityindex is shown. The method comprises the steps of exciting an embryowith an excitation energy from an excitation source at an excitationfrequency in step 401, acquiring a set of images of the embryo from animaging device in step 402, performing a Fourier Transformation on theset of images to generate a set of phasor coordinates in step 403,computing a D-trajectory of the phasor coordinates in step 404,computing a set of values of additional parameters from the set ofimages and the phasor coordinates in step 405, comparing the set ofvalues to a set of stored values related to embryos of known viabilityin step 406, and calculating a viability index factor of the embryo fromthe set of values and the set of stored values in step 407.

Experimental Examples

The invention is further described in detail by reference to thefollowing experimental examples. These examples are provided forpurposes of illustration only, and are not intended to be limitingunless otherwise specified. Thus, the invention should in no way beconstrued as being limited to the following examples, but rather, shouldbe construed to encompass any and all variations which become evident asa result of the teaching provided herein.

Without further description, it is believed that one of ordinary skillin the art can, using the preceding description and the followingillustrative examples, make and utilize the system and method of thepresent invention. The following working examples therefore,specifically point out the exemplary embodiments of the presentinvention, and are not to be construed as limiting in any way theremainder of the disclosure.

Safety

In order to ensure the safety of the FLIM imaged embryos, the optimumlaser power to avoid DNA damage, while allowing the rapid and robustacquisition of the FLIM signal on mouse pre-implantation embryos wasdetermined. 2-cell (E1.5) and morula (E2.5) stage CD1 and C57/BL6NCrlembryos were exposed to varying laser powers (1.5, 3.5, 10, and 15 mW)and the effect on the developmental progression of embryos until theblastocyst stage was examined (see FIG. 5A and FIG. 5B). With referenceto FIG. 5A, images 501 depict transmission images of embryos before andafter imaging and culturing for 72 hours till E4.5. Image size is708.49*708.49 μm. Graph 502 shows an assessment of embryonic developmentafter imaging and 72-hour in vitro culture reported as percentdevelopment. CD1: E1.5 non-imaged control (n=72); 1.5 mW (n=42); 3.5 mW(n=42), 10 mW (n=31), 15 mW (n=45) imaged embryos. C57BL/6NCrl: E1.5non-imaged control (n=37); 1.5 mW (n=37); 3.5 mW (n=36), 10 mW (n=38),(n=37) imaged embryos.

In order to capture FLIM-signals of embryos taken with 1.5 mW laserpower, 4 times longer exposure time was required than the embryoscollected at 3.5 mW, 10 mW, 15 mW laser powers due to their low signalto noise ratio. The majority of embryos exposed to 1.5 mW and 3.5 mWlaser power developed to the blastocyst and there were no significantdifferences between the control (non-imaged) and embryos imaged at the2-4cell stage or morula-compaction stage, irrespective of straindifferences (CD1 or C57BL/6NCrl) (see FIG. 5A and FIG. 5B). However, at10 mW, approximately 20% and 35% of CD1 embryos imaged at the 2-cell andcompaction stages, respectively, fail to progress to the blastocysts. At15 mW, nearly 50% of CD1 and C57BL/6NCrl embryos imaged at the 2-cellstage were arrested before the compaction stage, while approximately 30%of CD1 and 12% of C57/BL6NCrl embryos imaged at the compaction stagefailed to develop to blastocysts. It was concluded that CD1 embryos aremore sensitive to laser damage, and that 3.5 mW is the ideal laser powerfor FLIM analysis.

Next, the activation of the DNA repair pathway in the embryo wasexamined by conducting immunofluorescence staining foranti-phosphorylated Histone 2AX (H2AXs139), a novel marker forDNA-double strand breaks. Both the non-imaged control and FLIM-imagedembryos were indistinguishable and did not show any signs of DNA repairpathway activation at 3.5 mW. However, embryos exposed to 1.5 mW laserpower, which required longer laser exposure time (12 minutes, instead of˜3 minutes) showed the sign of DNA damage (see FIG. 5C).

With reference now to FIG. 5C, images 505 depict H2AX staining of FLIMimaged pre-implantation embryos. Two strains of embryos were FLIM imagedat E.1.5 stage and subjected to Hoechst (blue) and H2AX (red) stainingat E2.5 for DNA-damage assessment. Control: no FLIM FLIM-imaged embryos.For CD1: E2.5 control (n=3); 1.5 mW (n=3); 3.5 mW (n=3), 10 mW (n=3) and15 mW (n=3). For C57/BL6NCrl: E2.5 control (n=9); 1.5 mW (n=9); 3.5 mW(n=8), 10 mW (n=6), and 15 mW (n=8). Scale bar set to 201 μm. Graph 506shows live birth rates of FLIM-imaged embryos. Control (solid blue,n=88) and FLIM-imaged CD1 embryos (diagonal stripes, blue, n=94) wereallowed to develop to blastocysts and implanted into 13 pseudo pregnantmice (3 independent trials). The pups were collected from C-section onE18.5. There is no difference for the live birth rate (p-value, 0.662).

Finally, the effects of FLIM on the rate of pregnancy were examined.Specifically, FLIM-imaged at E2.5 and control BRE-gal embryos wereallowed to advance to early blastocyst stage (E3.5) and 12-16 controland FLIM-imaged embryos were implanted per pseudopregnant mother splitevenly between the left and right uterine horn. E18.5 embryos werecollected through caesarean section (C-section) and counted forimplantation efficiency (Graph 506 and Table 1, Table 2, and Table 3,below). The average live birth rates were 49% for FLIM-imaged group and43% for the non-imaged BRE-gal control group based on three independentexperiments (Table 1, Table 2, and Table 3). Student t-test reveals thatthere is no significant statistical difference between FLIM-imaged andnon-imaged groups. It was therefore concluded that FLIM imaging of themorula stage embryo at 3.5 mW excitation is safe to use and employed inthe subsequent experiments.

TABLE 1 CD-1 BRE-gal⁺⁻ Embryos Embryos CD-1 BRE-gal⁺⁻ ImplantedImplanted E18.5 E18.5 Trial 0 Mice 1  7  7 5/7 (71.4%) 4/7 (57.1%) Mice2  7  7 1/7 (14.3%) 1/7 (14.3%) Mice 3  7  7 4/7 (50%) 2/7 (25%) Mice 4 7  7 1/7 (12.5%) 4/7 (50%) Total 28 28 11/28 (39.3%) 11/28 (39.3%)Trial 1 Control 1  8  8 4/8 (50%) 6/8 (75%) Imaged 1  8  8 6/8 (75%) 2/8(25%) Imaged 2  8  8 2/8 (25%) 6/8 (75%) Imaged 3  8  8 1/8 (12.5%) 2/8(25%) Imaged 4  8  8 4/8 (50%) 4/8 (50%) Total Control  8  8 4/8 (50%)6/8 (75%) Total Imaged 32 32 13/32 (40.6%) 14/32 (43.8%)

TABLE 2 Trial 2 CD-1 BRE-gal⁺⁻ Embryos Embryos CD-1 BRE-gal⁺⁻ ImplantedImplanted E18.5 E18.5 Control 1 10 2 1/10 (10%) 1/2 (50%) Control 2 10 21/10 (10%) 0/2 (0%) Control 3 10 2 5/10 (50%) 1/2 (50%) Imaged 1 10 25/10 (50%) 2/2 (100%) Imaged 2 10 2 5/10 (50%) 0/2 (0%) Total Control 306 7/30 (23.3%) 2/6 (33.3%) Total Imaged 20 4 10/20 (50%) 2/4 (50%)

TABLE 3 Trial 3 CD-1 Bre-gal⁺⁻ Embryos Embryos CD-1 Bre-gal⁺⁻ ImplantedImplanted E18.5 E18.5 Control 1 14 NA 12/14 (85.7%) NA Control 2 14 NA3/14 (21.4%) NA Control 3 14 NA 6/14 (42.9%) NA Imaged 1 14 NA 8/14(57.1%) NA Imaged 2 14 NA 9/14 (64.3%) NA Imaged 3 14 NA 8/14 (57.1%) NATotal Control 42 NA 21/42 (50%) NA Total Imaged 42 NA 25/42 (59.5%) NAPre-Implantation Mouse Embryo Collection

Females at 21-24 days old were superovulated with pregnant mare serumgonadotropin (PMSG, Sigma) and 48 hours later with human chorionicgonadotropin (HCG, Sigma). Matings were set each evening after hCGinjections. The following morning a vaginal plug was considered 0.5 dayspost fertilization and embryos were collected at desired stages byflushing oviducts or uterine horns. For time course collection(intrinsic fluorescence FLIM and THG measurements) superovulation andmatings were staggered and all the embryos were collected the same dayexcept late blastocysts (E4.5) were generated by dissecting at E3.5 (oneday before imaging) and cultured till next day.

Embryo Culture

Embryos were cultured at 12.8% for hypoxia condition or 20.9% O₂(measured using Neofox oxygen sensor), with 5% CO₂ in nitrogen balanceat 37° C. The drop size used was on average ˜10 embryos/20 μl drop (1drop per dish) except for the prediction test, where the drop size wason average 1 embryo/3 μl drop (˜10 drops/dish). Embryos were culturedand imaged on Matek Glassbottom Dishes (P35G-1.5-14-C). Single embryocultures were used for embryo viability prediction (of 1 embryo per 3 μlof KSOMaa) to prevent the mobility of embryos and provide stableenvironment, to avoid ROS accumulation or influence of neighboringembryos, and to create a library for prediction and embryonicdevelopmental potential. All other experiments were performed on groupcultures ˜10 embryos/20 μl drop (1 drop per dish). A 3D segmentationpipeline was used (see e.g. Chiang et al., Analysis of in vivo singlecell behavior by high throughput, human-in-the-loop segmentation ofthree-dimensional images. BMC Bioinformatics 16, 397 (2015),incorporated herein by reference) to do a 3D reconstruction of embryosand conduct cell number analysis.

Inhibition of Oxidative Phosphorylation and Glycolysis:

Embryos were placed in 25 μl microdroplets of KSOMaa (Invitrogen) withthe appropriate inhibitors covered in mineral oil (Sigma). Both of thetwo chemical inhibitors, rotenone and antimycin A cocktail (R&A) and2-Deoxyglucose (2DeoxyG) were dissolved in KSOMaa. For R&A, theinhibitor was prepared to perform dose dependence measurements for afinal concentration of 100 nM and 500 nM. For 2DeoxyG, the inhibitor hasa final concentration of 1 mM. KSOMaa was used as a solvent and culturemedia for the control group and treatment group embryos.

H2AXs139 Staining

CD1 and C57BL/6NCrl post-imaged embryos are rinsed with Tyrode's acid(Sigma) 3 times and placed in holding and flushing media for 5 minutesto allow embryos to acclimate before 30-minute fixation in 4%paraformaldehyde on ice. Embryos were permeabilized using Triton X-100(Fisher). And then embryos were incubated with H2AXs139 (Genetex) at1:1000 for 1 hour at room temperature. Embryos were rinsed in 1× PBTthree times and then stained with AlexaFluor555 at 1:200. Embryos wererinsed in 1×PBS three times before processing for the Hoechst (Sigma)staining for 10 minutes to stain the DNA. Finally, embryos were rinsedand imaged in 1×PBS using 780 Zeiss microscope and Zen 2012 software.

Embryo Implantation and C-Section at E18.5

CD-1 female mice were mated with vasectomized males to generatepseudopregnant females timed to E3.5 for implantation. E2.5 embryos werecollected and imaged and implanted at E3.5. In each experiment, embryoswere randomized before imaging to non-imaged and FLIM-imaged groups.After imaging, non-imaged embryos and FLIM-imaged embryos wererandomized. The technician transferring the embryos was blinded to whichembryos were imaged or nonimaged. 12-16 embryos were implanted into theleft and right uterine horn of pseudopregnant females. E18.5 embryoswere collected through C-section and counted for the implantationefficiency. Genotyping was done for experiments that used BRE-gal +/−embryos to differentiate between WT embryos to BRE-gal +/− embryos.Embryos were genotyped with Tissue Direct Phire PCR Kit with thefollowing primers: (LacZ band) Fwd: 5′ ATG AGC GTG GTG GTT ATG C 3′ (SEQID NO:1), Rev: 5′ GAT GGT TCG GAT AAT GCG 3′ (SEQ ID NO:2); (Hprt band)Fwd: 5′ AAG CCT AAG ATG AGC GCA AG 3′ (SEQ ID NO:3), Rev: 5′ AAG CGA CAATCT ACC AGA GG 3′ (SEQ ID NO:4).

DCF-DA Staining

Embryos were rinsed in Acid Tyrode 3×, washed in KSOMaa 3×, transferredto 5 uM DCF-DA in 1× PBS. Embryos were incubated in DCF-DA solution for25 min at 5% CO₂ and 37° C. Embryos were then transferred to Hoechststain solution for 8 min. Then embryos were placed in KSOMaa and imagedwith LSM780 at 5% CO₂ at 37° C.

Fluorescence lifetime imaging microscopy (FLIM)

Fluorescence lifetime images of the pre-implantation embryos wereacquired on Zeiss LSM710 (Carl Zeiss, Jena, Germany), a multi-photonmicroscope coupled with a Ti: Sapphire laser (Spectra-Physics Mai Tai,Mountain View, CA) with 80 MHz repetition rate. The FLIM data detectionwas performed by the photomultiplier tube (H7422p-40, Hamamatsu, Japan)and a320 FastFLIM FLIMbox (ISS, Champaign, IL). The pre-implantationmouse embryos were excited at 740 nm; an average power of ˜3.5 mW wasused. A Zeiss EC Plan-Neofluar 20×/0.5 NA objective (Cart Zeiss, Jena,Germany) was used. The following settings were used for the FLIM datacollection: image size of 256×256 pixels, scan speed of 25.21 μs/pixel.A dichroic filter at 690 nm was used to separate the fluorescence signalfrom the laser light. And the emission signal was split with 496 nm LPfilter and detected in two channels using a band pass filter 460/80 anda 540/50 filter. Every FLIM image was acquired for 50 frames of the samefield of view with 256×256 per frame. Only the blue channel (460/80)data was used for this study. FLIM calibration of the system wasperformed by measuring the known lifetime of a fluorophore coumarin 6(dissolved in ethanol), which has a known fluorescence lifetime of T=2.5ns(37, 38). Embryos were kept in standard culture conditions, 37° C. andat 5% CO₂. FLIM data were acquired and processed by the SimFCS softwaredeveloped at the Laboratory of Fluorescence Dynamics (LFD).

FLIM information was gathered with the prototype as shown in FIG. 1 .The FLIM signature indicates the same trend as the previous literaturereported, from glycolysis to oxidative phosphorylation duringpre-implantation embryonic development. As shown in FIG. 6A, thetransmission image and the correlated FLIM images of thepre-implantation embryos from E1.5 (2-cell, 601), E25 (compacting 8-cellstage, 602), E3.0 (morula, 603), and E4.5 (late blastocyst, 604) stage.

Additional data is shown in FIG. 6B. FIG. 6B shows how the lifetimetrajectory of pre-implantation embryos correlates with embryonicdevelopment. The images A in FIG. 6B are Transmission (top row),fluorescence intensity (middle row, 740 nm excitation) and FLIM (bottomrow) images of representative pre-implantation CD1 mouse embryos at2-cell (E1.5), morula (E2.5), compaction (E3.0), early blastocyst(E3.5), and blastocyst stage (E4.5). In the FLIM images, the pseudocolor displays the fluorescence lifetime. Phasor-plot B depicts averagefluorescence lifetime of CD1 embryos at the indicated developmentalstages demonstrating the D-trajectory (D for development). Arrow 611indicates the fluorescence lifetime change from E1.5 to E2.5 and arrow612 shows the change from E3.0 to E4.5. (C-D) Scatter plots show theD-trajectory for CD1 and C57BL/6NCrl embryos. The small window shows theaverage and standard deviation of each stage. CD1: 2-cell (n=29), morula(n=11), compaction (n=33), early blastocyst (n=50) and blastocyst stage(n=35); C57BL/6NCrl: 2-cell (n=25), morula (n=22), compaction (n=21),early blastocyst (n=38) and blastocyst stage (n=42). Graph C shows theD-trajectory of CD1 embryos (2-cell, n=8; morula, n=8; compaction, n=12;early blastocyst, n=5; blastocyst, n=8. and graph D shows theD-trajectory of C57BL/6NCrl embryos (2-cell, n=12; morula, n=11;compaction, n=9, early blastocyst, n=8; blastocyst, n=7). N=number ofembryos analyzed.

Two different mouse strains (a non-inbred CD1 and an inbred C57BL/6NCrl)were used to acquire a comprehensive representation of the phasor-FLIMdistribution patterns of embryos during pre-implantation development(FIG. 6B and 701 in FIG. 7A). With reference to FIG. 6B, fluorescentlifetimes of endogenous fluorescent species, excited at 740 nm, werecollected at the 2-cell (E1.5), morula (E2.5), compaction (E3.0), earlyblastocyst (E3.5) and blastocyst stage (E4.5), and pseudo-coloredaccording to the phasor coordinates (A and B in FIG. 6B). The phasorcoordinates, which is the averaged fluorescent lifetime, of the 2-celland morula stage embryos have a unique lifetime distribution patterndistinct from all other cell and tissue types measured (arrow 611). Thisunique phasor lifetime position may reflect special characteristics oftotipotent cells, which mirror low oxygen consumption and preferentialutilization of pyruvate oxidation (see e.g. Shyh-Chang et al., Stem cellmetabolism in tissue development and aging. Development 140, 2535-2547(2013), incorporated herein by reference). On the other hand, compactionto blastocyst stages display average phasor coordinates typicallyobserved in pluripotent cells (arrow 612) (see e.g. Stringari et al.,Label-free separation of human embryonic stem cells and theirdifferentiating progenies by phasor fluorescence lifetime microscopy. JBiomed Opt 17, 046012 (2012), incorporated herein by reference). Thischaracteristic developmental time course lifetime distribution patternis referred to herein as the developmental trajectory or “D-trajectory”.Phasor-FLIM lifetime distributions of individual embryos from bothoutbred and inbred mouse strains, shown in graphs C and D in FIG. 6B,follow the similar developmental trend D-trajectory. In order to examinewhether the genetic background of mice influences the D-trajectory, thetrajectories of both CD1 and C57BL/6NCrl strains were compared (seegraphs C and D in FIG. 6B). While the average lifetimes (g and s values)at specific embryonic stages are somewhat variable, the overallD-trajectory distribution (arrows 611 and 612) of C57BL/6NCrl is similarto that of CD1 mice. It follows that the D-trajectory is acharacteristic distribution behavior observed among pre-implantationmouse embryos. In addition, time-lapse FLIM imaging was applied toindividual embryos (n=16), and continuously followed at 3-hour timeintervals from 2-cell (E1.5) to blastocyst stage (E4.5) forapproximately 60 hours. The in vitro developmental trajectory (Graphs702 in FIG. 7A) of each embryo mirrors the D-trajectory (Element B ofFIG. 6B). Lastly, the phasor-FLIM developmental patterns were comparedbetween the pre-implantation embryos cultured under ambient (20.9%oxygen) and oxygen-hypoxia condition (12.8% oxygen, trigas of 5% 02, 5%CO2, 90% N2 mixed with atmosphere) (703 in FIG. 7B). After 4 hours ofincubation, the 2-cell (E1.5), morula (E2.5), compaction (E3.0), earlyblastocyst (E3.5) and blastocyst stage (E4.5) embryos were subjected toFLIM collection of endogenous fluorescent species, excited at 740 nm.The D-trajectories of embryos were similar between embryos grown underthe ambient and hypoxic condition (arrows 611 and 612). The arrows wereobserved to shift slightly towards the right for the hypoxic condition,presumably due to the higher glycolysis rate. The shifts for the s and gcoordinates are not significant. In sum, two combined lifetimetrajectories (arrows 611 and 612) encompass the overall D-trajectory fornormal pre-implantation embryo development.

With reference to FIG. 7A, a development trajectory composed of twodifferent trajectories that correlate with the metabolism of embryonicstages. Graphs 701 are representative phasor plots for thepre-implantation mouse embryo from early cleavage stage to blastocyststage (E1.5-E4.5). Graphs 702 show four examples of the D-Trajectoryobserved throughout pre-implantation stages (from 2-cell to blastocyststages). With reference to FIG. 7B, FLIM trajectories of embryos grownunder ambient and hypoxic oxygen conditions are shown. Images 703 aretransmission and FLIM images plots for the pre-implantation mouse embryofrom early cleavage stage to blastocyst stage under 12.8% oxygen and20.9% oxygen conditions. Graph 704 is a D-Trajectory showing the sametrend for hypoxia condition (12.8% Oxygen, red) (n=19, 16, 11, 15, 13for E1.5, E2.5, E3, E3.5, E4.5 stage respectively) and regular bloodmixture culture condition (20.9% oxygen, blue) (n=29, 8, 11, 14, 11 forE1.5, E2.5, E3, E3.5, E4.5 stage, respectively). The straight lines showthe developmental pattern of the embryos. The starting scatter is thetop one for both groups represents E1.5. The last scatter of the linesis the left one for both groups represent E4.5. For g values of hypoxiatreatment group, compare with regular blood mixture treatment group,p-value=0.296, 0.018, 0.829, 0.488, 0.584 for E1.5, E2.5, E3, E3.5, E4.5stage, respectively (student t-test, two-tail test for g). The errorbars show the standard deviation for each condition. Scale bar set to100 μm.

Reactive oxygen species (ROS) play a key role in cellular metabolism andhomeostasis and ROS production has been linked to an increase inoxidized lipids. Arrow 612 in the D-trajectory is presumably due to anincreasing fractional contribution of ROS as well as the oxidized lipidswhich have a fluorescence lifetime distribution of 7.89 ns and fall onthe same published location (coordinates) of the semi-circle in thephasor plot (see 202 in FIG. 2 ). This behavior is consistent with themodel that an increase in aerobic respiration and metabolism as well as(3-oxidation during pre-implantation mouse development requires moreefficient energy production from oxidative phosphorylation. Thedisclosed experiments confirm the presence of active ROS production withfluorogenic marker 2′, 7′-dichlorofluorescin diacetate (DCF-DA, alsoknown as H2DCFDA) staining (see FIG. 8 ).

Third-Harmonic Microscopy

In order to better characterize the lipid droplets distribution duringembryonic development, third-harmonic generation (THG) microscopyimaging was employed (see FIG. 9 ) with a Deep Imaging Via EmissionRecovery (DIVER) microscope. The DIVER microscope is an upright laserscanning microscope with a wide photocathode area detector, which allowsfor collection of photons from a wide area and angle for highefficiency. The third harmonic generation images and intrinsicfluorescence FLIM images were collected using a 40× water immersionobjective (Olympus Plan Apo) with 1040 nm and 740 nm excitationrespectively. And UG11 and Blue5543 filters were used for THG andendogenous fluorescence FLIM images collection. An a320 FastFLIM FLIMbox(ISS, Champaign, IL) was used to transfer the data to the phasor plot.Rho110 was used for calibration with known lifetime τ=4 ns.

The interfaces heterogeneity can be detected with the third ordernonlinearity λ₃. Given that the process is ultra-fast for structureswith THG signals, the lifetime is approximately zero. Row A of imagesshows the representative THG intensity images acquired in the same fieldof view as that of the FLIM images of row B. The phasor plot of the THGimages appears at the coordinate of s=0 and g=1. Furthermore, theco-localization correlation of the long lifetime specie in the FLIMimages (red) was correlated with the lipid droplets (green) in THGimages (see rows C and D). During embryonic development, the oxidizedlipid signature, color-coded in red for the long lifetime species, (samedirection as red arrow 612) accumulated. The Mander's splitco-localization correlation coefficients increase from 0.0099 to 0.3907(where a coefficient of 1 is perfect correlation and 0 is complete lackof correlation) with embryonic development, suggesting that thephasor-FLIM distribution changes during these stages are due toincreased lipid accumulation. The lipid droplets distribution duringembryonic development was also characterized using the 3D THG image (seerow A and graph E in FIG. 9 ). Cleavage stage embryos have a largeamount of small, densely packed lipid droplets, whereas post-cleavagestage embryos have large lipid droplets of the low density. The dramaticchanges for both the lipid oxidation and lipid volume size start aftercompaction stages. These findings demonstrate that the dynamicdifference in lipid oxidation can be detected by phasor-FLIM.

Converting FLIM Data onto Phasor Coordinates:

All FLIM images are transformed onto the phasor plot using the equationsbelow. The g and s coordinates are generated from the fluorescenceintensity decay of each pixel in the FLIM image using the followingFourier transformation equations (see 201 in FIG. 2 ):

${g_{i}(\omega)} = \frac{\int_{0}^{\infty}{{I(t)}{\cos( {\omega t} )}dt}}{\int_{0}^{\infty}{{I(t)}{dt}}}$${s_{i}(\omega)} = \frac{\int_{0}^{\infty}{{I(t)}{\sin( {\omega t} )}dt}}{\int_{0}^{\infty}{{I(t)}{dt}}}$

Thus, the phasor approach is a fit-free analysis of FLIM imaging, andthe g and s coordinates represent the decay curve at each pixel of theimage. Therefore, a phasor analysis transforms the complicated spectrumand decay of each pixel into a unique position on the phasor plot.

Metabolic States in Embryos

With reference now to FIG. 10A, data is shown indicating that lifetimetrajectories reveal metabolic states of pre-implantation mouse embryos.Set of images A comprises transmission (top), fluorescence intensity(middle) and FLIM (bottom) images for control and 4-hour rotenone andantimycin A (R&A) treated embryos. Note a shift from long to shortlifetimes (blue to red in FLIM image). Graph B depicts g and s values ofcontrol and R&A-treated embryos for individual embryos. Blue circles arecontrols (n=38), red circles are R&A-treated embryos (n=31), and solidsquares and the error bars in the figures means the average andvariation of each group (student t-test for g value: p-value=2.86E-16).FLIM images indicate a rightward shift from long to short lifetimes. Setof images C comprises transmission (top), fluorescence intensity(middle) and FLIM (bottom) images for control and 2DeoxyG-treatedembryos. Note a shift from long to short lifetimes (red to white in FLIMimage). Graph D depicts g and s values of control and 2DeoxyG-treatedembryos. Blue squares are controls (n=12), red circles are2DeoxyG-treated embryos (n=13), and the average of each group can befound in the solid colored squares (student t-test for g value:p-value=3.88E-09). Fluorescence and FLIM images indicate a leftwardshift from long to short lifetimes.

The D-trajectory is complex because it is composed of lifetimes fromvarious endogenous fluorescent biochemical species. It was firsthypothesized that the major component responsible for the shifts in theD-trajectory was intracellular NADH changes based on its fundamentalrole in energy production during embryogenesis. To test this hypothesis,the metabolic activity of intracellular NADH was measured. The boundform of NADH is linked to energy production through oxidativephosphorylation, whereas the free form of NADH is associated withglycolysis (see e.g. C. Stringari et al., Metabolic trajectory ofcellular differentiation in small intestine by Phasor FluorescenceLifetime Microscopy of NADH. Sci Rep 2, 568 (2012), incorporated hereinby reference). The phasor coordinates of free NADH maps on the rightside of the plot with a lifetime of 0.38 ns and the protein bound formof NADH (bound with lactate dehydrogenase) maps on the left at 3.4 ns(see 202 in FIG. 2 ). This lifetime distribution of the free and boundforms of NADH in the phasor plot have previously been described as themetabolic or M-trajectory.

Next, embryos were treated with known biochemical inhibitors ofoxidative phosphorylation and glycolysis. Oxidative phosphorylation wasinhibited at the early compaction stage with a cocktail of rotenone andantimycin A (R&A) (500 nM) by inhibiting complex I and complex III ofthe electron transport chain. Embryos were imaged after a 4-hour cultureperiod (images A in FIG. 10A). The FLIM images show increased fractionalcontributions of free NADH (shorter lifetimes) when compared to controls(left side images A in FIG. 10A). This shift towards glycolyticmetabolism is seen in a dose-dependent manner (see FIG. 10D), indicatingthat embryos cultured in R&A have decreased oxidative phosphorylationactivities (image A and graph B in FIG. 10A). The early blastocyst stageembryos were also cultured in 1 mM 2-Deoxy-D-Glucose (2DeoxyG), ananalog of glucose, to inhibit glycolysis (images C of FIG. 10A). Theglucose analog treatment shifted the phasor-FLIM distribution to longerlifetime (an increase of bound NADH), which correlates with a decreasein glycolysis (images C and graph D of FIG. 10A). These findings suggestthat the source of the changes seen in the phasor coordinates throughoutthe pre-implantation stages in the D-trajectory is in part due to thecontribution from metabolic shifts of NADH.

With reference to FIG. 10D, the depicted images and graph demonstratethat fluorescence lifetime trajectories reveal dose-dependent metabolicstate changes of pre-implantation mouse embryos. Images 1011 showtransmission (top), fluorescence (middle) and FLIM (bottom) images forcontrol and 4-hour 100 nM and 500 nM rotenone and antimycin A (R & A)treated embryos, indicating a shift from long to short lifetimes. Graph1012 shows g and s values of control and 4-hour 100 nM and 500 nMR&A-treated embryos for individual embryos. Blue circles are controls(n=38), red circles are 4-hour 100 nM R&A-treated embryos (n=21), andgreen circles are 4-hour 500 nM R&A-treated embryos (n=31). The averageof each group can be found in the solid squares (for g value of 100 nMtreatment group and 500 nM group compared with control group,p-value=1.76E-5, and 2.86E-16, respectively). FLIM images indicate arightward shift from long to short lifetimes. Student t-test andtwo-tail tests were performed.

The disclosures of each and every patent, patent application, andpublication cited herein are hereby incorporated herein by reference intheir entirety. While this invention has been disclosed with referenceto specific embodiments, it is apparent that other embodiments andvariations of this invention may be devised by others skilled in the artwithout departing from the true spirit and scope of the invention. Theappended claims are intended to be construed to include all suchembodiments and equivalent variations.

Identifying Pre-Implantation Embryos Under Stress Conditions

Given that early cleave stage embryos utilize aspartate, pyruvate, andlactate for energy metabolism the next experiment sought to determinewhether the unique lifetime distribution patterns of an embryo culturedunder altered physiological states can be detected by the changes inspectroscopic distributions of phasor-FLIM.

With reference to FIG. 10B and FIG. 10C, images A show transmissionimages of embryos collected at the 2-cell stage and cultured in KSOMaa,FHM, or PBS for 24 hours. Images B are representative transmission andFLIM images of embryos in KSOMaa, FHM, or PBS for 4 hours. Graph C inFIG. 10C is a scatter plot of g and s lifetimes collected from a groupof embryos cultured in KSOMaa (n=10), FHM (n=10) and PBS (n=4) for 4hours. p-value=0.0002** and 0.01* (student t-test of g value) for theFHM and PBS group compare with KSOMaa group. Images D are transmissionimages of embryos collected at the morula stage and cultured in KSOMaa,FHM, or PBS for 24 hours. Images E (FIG. 10B) are representativetransmission and FLIM images of embryos in KSOMaa, FHM, or PBS for 4hours. Graph F in FIG. 10C is a scatter plot of g and s of lifetimescollected from a group of embryos cultured in KSOMaa (n=8), FHM (n=8),and PBS (n=8). p-value=9.29E-06** and 3.21E-07** (student t-test of gvalue) for the FHM and PBS group compare with KSOMaa group.

2-cell and morula stage embryos were cultured in standard mouse embryoculture media (KSOMaa), flushing and holding media (FHM: DMEM-pyruvatefree with HEPES), and saline solution (PBS). Brightfield images and FLIMdata were collected at 4 hours and 24 hours after the treatment (FIG.10B). The FLIM data were collected once at the first time-point (4hours). The 2-cell stage embryos cultured under KSOMaa, FHM and PBS weremorphologically normal (Images A, top row of FIG. 10B). However, theembryos in high-stress conditions (FHM and PBS) show distinct lifetimedistribution patterns on the phasor-plot when compared to that of KSOMaacultured embryos (Images B of FIG. 10B and graph C of FIG. 10C, Graph Aof FIG. 10E). Subsequently, the embryos under high-stress conditionswere found to fail to cleave normally and remain at the 2-cell stage,unlike KSOMaa controls (images B in FIG. 10B, Graph A of FIG. 10E).Similar analysis using compaction stage embryos was performed, and itwas observed that within a few hours under high-stress cultureconditions, the phasor-FLIM lifetime trajectories of embryos deviatefrom those cultured in KSOMaa even before the embryos show any signs ofabnormal morphology (Images D and E of FIG. 10B, graph F of FIG. 10C,Graph B of FIG. 10E). The cell division in FHM and PBS cultured embryosalso slowed down significantly (Graph B of FIG. 10E).

With reference to FIG. 10E, two graphs A and B are shown depicting theaverage number of cells per embryo cultured under high stressconditions. Bar graph A shows the average number of cells present in anembryo after xx hours of culturing in the indicated media starting atthe 2-cell stage. KSOMaa non-image control (n=8), KSOMaa (n=10), FHM(n=11), PBS (n=11), compare to the results from KSOMaa group,p-value=0.004** and 0.001** for FHM and PBS, respectively. Bar graph Bshows the average number of cells per embryo after continuous culturingof embryos in the indicated media starting at the morula stage. KSOMaanon-image control (n=10), KSOMaa (n=18), FHM (n=11), PBS (n=11),p-value=0.002** and 0.02* for FHM and PBS, respectively.

It was concluded that phasor-FLIM is a sensitive method to detect thechanges in embryo metabolism upon cellular stress.

Derivation of the Embryo Vitality Index

The phasor distribution analysis of pre-implantation mouse embryosallows differentiation between normal and highly stressed embryos (seeFIG. 11A and FIG. 11B).

With reference now to FIG. 11A, element A shows a schematic of theexperimental setup used to generate the subsequent data. Individualembryos (A-F) were followed from the 2-cell to blastocyst stage andclassified as healthy (H) and unhealthy (UH) group according to theirmorphology at E4.5. Histogram B is a graph of embryo viability index(EVI) of early compaction embryos from one representative experiment (Hgroup, n=37; UH group, n=27). The blue and red bars represent the embryocondition determined as healthy and unhealthy at ˜60 hours after FLIMimaging at the pre-compaction stage. Graph C is a receiver operatingcharacteristic (ROC) curve, showing the performance of the binaryclassification model developed from lifetime distribution patterns ofearly developmental stage embryos (2-cell, 4-cell, and early compactionstage). The area under the curve for each stage is 0.739 (2-cell), 0.728(4-cell) and 0.916 (early compaction). The dashed line in the diagonalis presented as a random bi-classification model. With reference to FIG.11B, Element D is a schematic of a FLIM-Distance Analysis Pipeline.Box-whisker plots E show a training set of healthy (n=5) and unhealthy(n=7) groups and tested unknowns of healthy (n=18) and unhealthy (n=16)embryos. Bar graph F shows the embryo viability index of the embryosshown in element E. The training set H is in navy, training set UH is inred. Testing set H is in light blue, and Testing set U is in orange.

The results show that the developmental potential of pre-implantationembryos is predictable through phasor-FLIM analysis. Time-lapsephasor-FLIM imaging of embryos was performed from the 2-cell stage for˜60 hours to identify the most desirable stage to predict thedevelopmental potential of embryos (element A). At the end of the60-hour culture period, embryos were classified as healthy (H) if theyreached the normal full expanded blastocyst stage showing a tightlypacked ICM and cohesive epithelium shaped TE cells, or not healthy (UH)if embryos were arrested before reaching the blastocyst stage ordisplaying abnormal blastocyst morphology (element A). The distanceanalysis (DA) algorithm was then applied to identify key spectroscopicparameters that could differentiate healthy (H) from unhealthy (UH)embryos by machine learning (see Ranjit et al., Characterizing fibrosisin UUO mice model using multiparametric analysis of phasor distributionfrom FLIM images. Biomedical Optics Express 7, 3519-3530 (2016),incorporated herein by reference).

Using the DA algorithm, the 3D phasor histogram was separated into 4sections based on the phasor coordinates (g, s) intensity, from which, 6parameters were extracted from each section, generating a total of 24parameters. The healthy embryos (H group) were used as the control setand the unhealthy embryos (UH group) were used as the sample set. Eachof these sets included images from multiple embryos from each stage indevelopment. Next, the average and variance of the training set werecalculated, which includes two groups (H and UH), and weighted 20parameters (g, s, the secondary moment a, b and angle from 4 sub-layers,intensity excluded) in each set from the 3D phasor plot. Afteroptimizing the weights to maximize the difference between unhealthy andhealthy group embryos, the weights were applied to index a new scorecalled the EVI or Embryo Viability Index. This partition metric definesthe degree of separation of the test embryos from the average of thetraining set where −1 to −10 are unhealthy embryos, and +1 to +10 arehealthy embryos.

Next, the DA data from 2-cell, 4-cell, and the early compaction stagewas examined to determine the best binary classification model usingreceiver operating characteristic (ROC) curves (Graphs B and C in FIG.11A, Graphs A and B in FIG. 11C). The embryos predicted to be healthywere classified in positive values (EVI<0, in blue), and embryospredicted to be unhealthy in negative values (EVI>0, in red). The plotof true positive rates against false positive rates gives an area underthe ROC curve (AUC) for 2-cell, 4-cell, and the early compaction stageembryos, which were 0.739, 0.728, and 0.916, respectively. It wastherefore concluded that the spectroscopic characteristics of the earlycompaction stage embryos (prediction accuracy with the highest AUC)possess the best parameters for separating embryos that will developinto normal blastocysts (Graphs B and C in FIG. 11A, Graphs A and B inFIG. 11C).

With reference to FIG. 11C, the presented data shows that the embryoviability index of morula shows the potential to distinguish healthy andunhealthy pre-cleavage stage embryos. Graphs A are histograms of embryoviability index of 2-cell and morula stage embryos from onerepresentative experiment (2-cell EVI: H group, (n=18), UH group,(n=17). 4-cell EVI, H group, (n=25), UH group, (n=9)). Each blue and redbar represents the morula stage FLIM-fingerprints of healthy (H) andunhealthy (UH) embryos at 60 hours after imaging respectively. Graphs Bare receiver operating characteristic (ROC) curves showing theperformance of the binary classification model developed from lifetimedistribution patterns of pre-compaction stage embryos (2-, 4-, and earlycompaction) of two time-lapse FLIM tracking experiments. The area undercurve for each stage is 0.777 (2-cell, H n=37; UH, n=8), 0.823 (4-cell,H, n=45; UH, n=8) and 1.000 (early compaction, H, n=30; UH, n=2) forexperiment 2, and 0.777 (2-cell, H, n=38, UH, n=10), 0.813 (4-cell, H,n=39, UH, n=7) and 0.945 (early compaction, H, n=39, UH, n=6) forexperiment 3.

An embryo viability prediction pipeline was developed based on the DA ofphasor-FLIM images of the early compaction stage embryos (Diagram D,FIG. 11B). FLIM images of embryos at the early compaction stage wererecorded and all said embryos were allowed to develop to the blastocystequivalent stage. The resulting embryos were classified as H or UH. Asmall number of healthy (H) and unhealthy (UH) embryos were thenselected as an EVI training data set. The remaining unselected embryoswere also subjected to the DA program as “unknowns” (test set) to testthe predictability of EVI. The development of 35 morphologically healthylooking early compaction stage embryos (pooled from 4 mating pairs) wasfollowed and recorded, until the blastocyst stage (Graphs E and F, FIG.11B). Of the 34 embryos, 18 developed to normal blastocysts and thusassigned as healthy (H), and 16 embryos that failed to reach theblastocyst were assigned as unhealthy (UH). When EVIs that weredetermined by the training set were applied, 83.3% of healthy embryos(15 out of 18 embryos) and 75.0% of unhealthy embryos (12 out of 16embryos) were correctly identified by EVI (Graphs E and F).Subsequently, four biologically independent experiments were performedusing a total of 134 embryos, with the results shown in Table 4 belowand FIG. 11D. The results show 85.9% accuracy (n=134) where a total of88.5% healthy embryos (n=96) and 73.7% unhealthy embryos (n=38) wereidentified. Based on the results, it was concluded that the DA programis able to predict the development potential of pre-implantation embryosat the early compaction stage.\

With reference to FIG. 11D, Box-whisker plots C show training sets ofhealthy (H) and unhealthy (UH) groups and predication performance ontested embryos for 4 additional experiments. The training set H is innavy, Training set UH in red, predicated healthy in light blue, andpredicated unhealthy in orange. The n number for the training sethealthy group was n=11, 2 and 3. The n number for the training setunhealthy group was n=4, 3, 2, and 2. The n number for the test unknownhealthy group was n=27, 25, 13, and 13. The n number for the testunknown unhealthy group was n=8, 4, 7, and 3. Bar graphs D show theembryo viability index of experiments 2, 3, 4 and 5. The training set His in navy, Training set UHs in red, Healthy in light blue, andUnhealthy in orange.

TABLE 4 False True Positive Positive Rate Rate Accuracy PrecisionSensitivity Specificity Experiment 1 0.250 0.833 0.794 0.789 0.833 0.750Experiment 2 0.375 0.852 0.800 0.885 0.852 0.625 Experiment 3 0.5000.920 0.862 0.920 0.920 0.500 Experiment 4 0.000 0.846 0.900 1.000 0.8461.000 Experiment 5 0.333 1.000 0.938 0.929 1.000 0.667 0.292 0.890 0.8590.905 0.890 0.708Statistical Analysis

Data are presented as mean±standard deviation. For the FLIM data, thestatistical analyses were performed using student t-test for the g valueonly, p<0.05 was considered as statistically significant.

The box-whisker plot showing the prediction ability represents themedian ±min/max from each indicated group (Training set H and UH group,Tested H and UH group).

DISCUSSION

The disclosed experiments show that phasor-FLIM represents a promisingnew approach for assessing the quality of pre-implantation mouseembryos. The phasor-FLIM analysis was applied to capture developmentalstates during pre-implantation development. The spectroscopictrajectory, referred to herein as the “D-trajectory” (D fordevelopment), is attributed to a combination of metabolic fluorescentspecies and production of ROS in conjunction with oxidized lipidmetabolism within the embryo (see graphs C and D, FIG. 6B), and thistrajectory correlates well with other measurements of embryonicdevelopment. Second, the intrinsic lifetime trajectory ofpre-implantation embryos cultured in nutrient-deficient media deviatesfrom the normal lifetime distribution, indicating that the lifetimetrajectory can be used to detect metabolic changes in embryos. Third,the applied DA program uses spectroscopic parameters from 3D phasorhistograms of embryos, and shows that EVI is a non-morphological,quantitative index that can provide useful information on the quality ofpre-implantation embryos.

Results

By implementing the phasor-FLIM information into a Distance MachineLearning Program, 86% (n=133) prediction accuracy was achieved for mouseembryo developmental potential prediction. The results are shown in FIG.11B. The data for the training set is shown in box and whisker plot 1101for healthy embryos and plot 1102 for unhealthy embryos. The data forthe test set is shown in box and whisker plot 1103 for healthy embryosand plot 1104 for unhealthy embryos. 93% of the training set and 89% ofthe test set were correctly predicted. Data was measured at thecompaction stage, which is 48 hours ahead of the traditional observationtime point. Thus, FLIM analysis provides a potential new format forselecting healthy embryos for implantation and represents an improvementover the state of the art in ART. The disclosed imaging device isadvantageous over existing devices due to the collection ofnon-invasive, quantitative and real-time metabolic information.

What is claimed is:
 1. A method for determining one or more propertiesof a tissue, comprising: exciting a tissue with an excitation energyfrom an excitation source at an excitation frequency; acquiring a set ofimages of the tissue from an imaging device; performing a FourierTransformation on the set of images to generate a set of phasorcoordinates; computing a D-trajectory of the phasor coordinates;computing a set of values of additional parameters selected from thegroup consisting of coordinates for a center of mass g and s, secondaxial moments a and b after diagonalization, an angle of distributionfrom diagonalization, and a total number of pixels in the phasor plotfrom slices derived from the phasor coordinates from the set of imagesand the phasor coordinates; comparing the set of values to a set ofstored values related to tissues having one or more knowncharacteristics; and calculating one or more properties of the tissuefrom the set of values and the set of stored values.
 2. The method ofclaim 1, wherein the values of additional parameters comprise changes inmetabolism of at least one cell in the tissue, and the properties of thetissue are selected from the group consisting of cell cycle, stress,cancer, and diabetes.
 3. The method of claim 1, wherein the tissue istaken from a blood sample, and the properties of the tissue areindicative of a neurodegenerative disease.
 4. The method of claim 1,wherein the excitation energy is selected from the group consisting oflaser light, LED light, photon laser light, photon excitation laserlight, and diffuse light.
 5. The method of claim 1, wherein theexcitation energy source is selected from the group consisting of alaser source, an LED source, and a lamp.
 6. The method of claim 1,further comprising a step of preventing excessive heating of the embryoby the excitation source with an excitation cleaner.
 7. The method ofclaim 6, wherein the excitation cleaner is selected from the groupconsisting of a heat blocker, a bandpass filter, a low-pass filter, ahigh-pass filter, a filter wheel, and beam splitter glass.
 8. The methodof claim 1, wherein the imaging device is selected from the groupconsisting of a modulated CMOS camera, a modulated EMCCD, an intensifiedCCD, a PMT, an APD, and a SPAD.
 9. The method of claim 1, wherein theimaging device comprises a microscope.
 10. The method of claim 1,wherein the imaging device is selected from the group consisting of ascanning device, a camera, a FLIM acquisition peripheral, and a pointdetector.
 11. The method of claim 1, wherein the imaging device is afluorescence lifetime imaging microscope.
 12. The method of claim 1,further comprising the step of modulating the excitation source at afrequency in a range from 1 MHz to 1 GHz.
 13. The method of claim 1,further comprising the step of acquiring multiple images from theimaging device simultaneously, wherein the imaging device is a CMOSimaging device having a plurality of taps.
 14. The method of claim 13,wherein the imaging device is selected from a modulated CMOS camera, amodulated EMCCD, an intensified CCD, a PMT, an APD, or a SPAD.